Publications & Resources
The Role of Probability-Based Inference in an Intelligent Tutoring System
Robert J. Mislevy and Drew H. Gitomer
Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, troubleshooting, and medical diagnosis. Based on an instructional tutoring system for learning to troubleshoot a military F-15 aircraft hydraulics system, the authors in this study explore the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probablity based reasoning in practical ITS context.
Mislevy, R. J., & Gitomer, D. H. (1996). The role of probability-based inference in an intelligent tutoring system (CSE Report 413). Los Angeles: University of California, Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).